Acoustic style transfer is motivated from the original image style transfer. Based on the fact that acoustic signals can be easily converted to visual images such as spectrograms and the spectrograms can represent the audial features visually, they applied the image style transfer techniques onto the acoustic spectrogram [1]}. In the spectrogram, the audio signals are converted/visualized as heat-maps where the horizontal axis represent frames (or time), vertical axis represent frequencies, and the color or intensity of the pixels represent the strength of the signal in the given time and frequency. The existing acoustic style transfer approaches use a similar method to extract the style features but adapted to audio by defining a vertically narrow convolutional feature maps or adopts an auto-encoder based approach [2]}. With the maps, the transfer model can capture acoustic signature across multiple frequency, ranging within a short amount of time. The style features are obtained by feeding style sound to pre-defined VGG models[3]} or a random noise layers [4]}, and then calculating a gain matrix of the activations. The use of random noise as filters was proven to work with image style transfer, as the textures share visual elements with simple random noises.
